对象唤醒：从单个图像中装配3D对象
Object Wake-up: 3D Object Rigging from a Single Image
摘要：
给定一个普通物体（如椅子）的单一图像，我们是否也可以恢复其类似于人类建模的三维形状，以便动画化其合理的关节和各种运动？这是一个有趣的新问题，可能具有许多下游增强现实和虚拟现实应用。与以前的工作相比，对象操作，我们的工作超越了二维操纵和刚性变形，并涉及关节操纵。为了实现这一目标，我们提出了一种自动化的方法来建立这样的3D通用对象从单个图像和嵌入关节骨架。具体来说，我们的框架首先从输入图像重建3D对象。之后，提取骨架的通用3D对象，我们开发了一种新的骨架预测方法，通过利用深隐函数的骨架概率场估计的多头结构。收集具有地面实况注释骨架的通用3D对象的数据集。从经验上讲，我们的方法在公共数据集以及我们的内部数据集上表现出令人满意的性能;我们的结果在3D重建和骨架预测方面都明显优于最先进的结果。
英文摘要：
Given a single image of a general object such as a chair, could we also restore its articulated 3D shape similar to human modeling, so as to animate its plausible articulations and diverse motions? This is an interesting new question that may have numerous downstream augmented reality and virtual reality applications. Comparing with previous efforts on object manipulation, our work goes beyond 2D manipulation and rigid deformation, and involves articulated manipulation. To achieve this goal, we propose an automated approach to build such 3D generic objects from single images and embed articulated skeletons in them. Specifically, our framework starts by reconstructing the 3D object from an input image. Afterwards, to extract skeletons for generic 3D objects, we develop a novel skeleton prediction method with a multi-head structure for skeleton probability field estimation by utilizing the deep implicit functions. A dataset of generic 3D objects with ground-truth annotated skeletons is collected. Empirically our approach is demonstrated with satisfactory performance on public datasets as well as our in-house dataset; our results surpass those of the state-of-the-arts by a noticeable margin on both 3D reconstruction and skeleton prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
